A number of techniques are known for recognizing three-dimensional objects from two-dimensional images of the objects. Brooks, in "Model-based 3D interpretation of 2D images," IEEE Trans. on Pattern Analysis and Machine Intelligence, 5(2):140-150, 1983, describes the use of parameterized objects for object recognition. There the objects are generalized cylinders.
Solina et al., in "Recovery of parametric models from range images," IEEE Trans. on Pattern Analysis and Machine Intelligence, 12(2):131-146, 1990, describe how to recover parametric objects from range data by minimizing surface distances, for example, using superquadrics with global deformations. There is no object recognition here.
Dickenson et al. survey a number of different shape recovery techniques in "From volumes to views: An approach to 3D object recognition," Computer Vision, Graphics and Image Processing: Image Understanding, 55(2):130-154, 1992.
Most prior art template matching methods do not use full volumetric templates. The templates are either two-dimensional for image matching, or in the case of range data, the templates are surface based. In addition, the prior art templates are relatively unconstrained to maximize the likelihood of matching. Unconstrained templates require the manipulation of a large number of parameters.
Therefore, it is desired to provide a vision-based recognition system that can identify physical objects having multiple constituent parts. Furthermore, it is desired to parse the identified objects so that they can be animated in virtual environments.